Downscaling

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Global climate models (GCMs) are run at coarse spatial resolution (typically of the order 50,000 km²) and are unable to resolve important sub-grid scale features such as clouds and topography. As a result GCMs can’t be used for local impact studies. To overcome this problem downscaling methods are developed to obtain local-scale surface weather from regional-scale atmospheric variables that are provided by GCMs. Wilby and Wigley (1997) were divided downscaling into four categories: regression methods, weather pattern-based approaches, stochastic weather generators and limited-area modeling. Among this approaches regression methods are preferred because of its ease of implementation and low computation requirements.

[edit] References

• Wilby, R.L. and Wigley, T.M.L., (1997) Downscaling general circulation model output: a review of methods and limitations, Progress in Physical Geography, 21, 530–548.
• Wilby, R.L., Dawson, C.W. and Barrow E.M., (2002) SDSM - a decision support tool for the assessment of regional climate change impacts, Environmental Modelling & Software, 17, 147– 159.
• Kim, J.W., Chang, J.T., Baker, N.L., Wilks, D.S., Gates, W.L., 1984. The statistical problem of climate inversion: determination of the relationship between local and large-scale climate. Monthly Weather Review 112, 2069–2077.
• von Storch, H., Zorita, E., Cubasch, U., 1993. Downscaling of global climate change estimates to regional scales: an application to Iberian rainfall in wintertime. Journal of Climate 6, 1161–1171.
• Hessami, M., Quarda, T.B.M.J., Gachon, P., St-Hailaire, A., Selva, F. and Bobee, B., “Evaluation of statistical downscaling method over several regions of eastern Canada”, 57th Canadian water resources association annual congress, 2004.

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